How KartaCam powers GrabMaps

Summary

Grab's Geo team required an efficient, low-cost way to collect fresh street-level imagery across Southeast Asia, where professional mapping equipment is prohibitively expensive and smartphone crowdsourcing yields inconsistent quality. To address this, Grab built KartaCam, a custom mapping device equipped with a 12MP sensor, dual-band GNSS, 4G LTE, and onboard edge AI. Edge machine learning models evaluate scene suitability, check image quality, filter for map-relevant objects, and blur personal data directly on the device prior to upload. Deploying a four-camera KartaCam 360 array delivers panoramic coverage and point-of-interest data comparable to commercial mapping rigs at roughly one-twentieth the hardware cost.

Takeaways

1. 1

   Edge AI algorithms run locally on KartaCam to discard low-quality, indoor, or rainy images and blur faces and license plates before any data leaves the device.

2. 2

   Using a dual-band GNSS module supporting both GPS L1 and L5 frequencies alongside a tuned ceramic antenna significantly mitigates multipath interference and positioning errors.

3. 3

   Benchmark testing in Singapore and Jakarta showed KartaCam 360 captures point-of-interest information comparable to professional mapping cameras that cost twenty times more.